P.155 Giant aneurysm, tiny patient: flow diversion stenting of a giant MCA aneurysm in a young child
Bibliographic record
Abstract
Background: A 3-year-old girl presented with a 6-day history of severe headaches. On examination, upper motor neuron signs were noted in the left upper and lower extremities with increased tone, reflexes, and a positive Babinski sign. MRI of the brain revealed a giant right middle cerebral artery (MCA) aneurysm with significant mass effect, associated with cerebral edema and ventricular effacement. CT and CT angiogram showed evidence of aneurysmal wall calcification and lamellar thrombosis within the aneurysmal sac. In addition, there was a smaller right MCA aneurysm in close proximity to the giant aneurysm. Methods: After a balloon occlusion test to assess collateral blood flow to the MCA territory, it was decided to treat both aneurysms with a flow diverting stent. Dual antiplatelet loading was done with aspirin and clopidogrel. The smallest available diameter of Pipeline Shield stent was deployed. Results: The patient remained neurologically unchanged. Early follow-up imaging demonstrated stent patency, reduced size and mass effect of the large aneurysm, reduced cerebral edema, and no flow into the smaller aneurysm. Conclusions: Flow diversion stenting may be employed successfully in pediatric patients, though has unique technical considerations including small size vessels and limited evidence for antiplatelet agent choice and dosing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".